Bibliographic record
Abstract
Between 2013 and 2014, PED virus (PEDv) swept through American pig farms, killing millions of animals and causing a market panic that drove the prices of both physical pork and lean hog futures to all-time highs. However, a divergence between pricing in financial markets and on-farm realities allowed some producers to reap record profits via a unique form of biological arbitrage. This arbitrage was novel in that it allowed for an underlier (pigs) to be used to profit from fluctuations in the price of a derivative (lean hog futures). This article explores the case of PEDv to examine the entanglements and divergences between ‘real’ and ‘abstract’ values in financialized industries, paying particular attention to the schisms between the imaginaries and practices of actors in the financial and tangibly productive links of the agricultural value chain. To do so, it examines the historical co-constitution of American agriculture and the financial sector, and shows how in the contemporary moment these two ever-more-intertwined sectors are nonetheless marked by important differences. It argues that the nature of agricultural production can confound the expectations of finance, and highlights the fact that financialization entails contextually-specific practices that can lead to uneven and unexpected market outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".